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Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation: 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Virtual Event, October 18-20, 2021, Revised Selected Papers

1, 2022

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Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI: 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, August 26-28, 2020, Revised Selected Papers

1st Edition 2020, 2020

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Visualization as a Service for Scientific Data

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, p.157-174

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Applying Recent Machine Learning Approaches to Accelerate the Algebraic Multigrid Method for Fluid Simulations

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.40-57

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Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.411-419

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The Convergence of HPC, AI and Big Data in Rapid-Response to the COVID-19 Pandemic

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.157-172

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Improving the Performance of the GMRES Method Using Mixed-Precision Techniques

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, 2021, p.51-66

2021

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Atomic Defect Identification with Sparse Sampling and Deep Learning

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.455-463

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Randomized Multilevel Monte Carlo for Embarrassingly Parallel Inference

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.3-21

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Large-Scale Neural Solvers for Partial Differential Equations

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, p.20-34

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High-Performance Ptychographic Reconstruction with Federated Facilities

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.173-189

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Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.22-39

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Fulfilling the Promises of Lossy Compression for Scientific Applications

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, 2021, p.99-116

2021

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Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.79-98

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NVIDIA’s Cloud Native Supercomputing

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.340-357

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Lessons Learned on the Interface Between Quantum and Conventional Networking

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.262-279

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Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.99-118

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Software Framework for Federated Science Instruments

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, p.189-203

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Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.58-75

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Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, p.190-205

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DataStates: Towards Lightweight Data Models for Deep Learning

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, p.117-129

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Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI: 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, August 26-28, 2020, Revised Selected Papers

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Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation: 21st Smoky Mountains Computational Sciences and Engineering, SMC 2021, Virtual Event, October 18-20, 2021, Revised Selected Papers

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI: 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, August 26-28, 2020, Revised Selected Papers

Visualization as a Service for Scientific Data

Applying Recent Machine Learning Approaches to Accelerate the Algebraic Multigrid Method for Fluid Simulations

Recurrent Multi-task Graph Convolutional Networks for COVID-19 Knowledge Graph Link Prediction

The Convergence of HPC, AI and Big Data in Rapid-Response to the COVID-19 Pandemic

Improving the Performance of the GMRES Method Using Mixed-Precision Techniques

Atomic Defect Identification with Sparse Sampling and Deep Learning

Randomized Multilevel Monte Carlo for Embarrassingly Parallel Inference

Large-Scale Neural Solvers for Partial Differential Equations

High-Performance Ptychographic Reconstruction with Federated Facilities

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression

Fulfilling the Promises of Lossy Compression for Scientific Applications

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks

NVIDIA’s Cloud Native Supercomputing

Lessons Learned on the Interface Between Quantum and Conventional Networking

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

Software Framework for Federated Science Instruments

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

DataStates: Towards Lightweight Data Models for Deep Learning

Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI: 17th Smoky Mountains Computational Sciences and Engineering Conference, SMC 2020, Oak Ridge, TN, USA, August 26-28, 2020, Revised Selected Papers

Enabling ISO Standard Languages for Complex HPC Workflows

Use It or Lose It: Cheap Compute Everywhere

Finding Novel Links in COVID-19 Knowledge Graph Using Graph Embedding Techniques

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